1. Technical Field
The present application relates generally to data classification and, more particularly, to an apparatus and method for improving recognition accuracy in handwritten text recognition systems by augmenting character data of collected handwriting samples.
2. Description of the Related Art
Currently, the need for accurate machine recognition of handwritten text has increased due to the popularity and wide spread use of handheld, pen-based computers. However, achieving high accuracy for handwriting recognition in conventional devices has proven to be a difficult task due to the wide variety of handwriting styles, many of which have ambiguous and/or conflicting character representations. In order to combat this problem, various techniques have been developed to enable handwriting recognition devices to adapt to an individual's writing style. These conventional methods can generally be divided into two categories: methods which require the collection of handwriting samples from each person; and methods which do not require such samples.
Typically, the conventional recognition systems which utilize handwriting samples are preferably used due to their superior recognition performance. However, one inherent problem with collecting these handwriting samples is that it is typically a tedious and burdensome process for the writer. In order to mitigate this burden and encourage the collection of the required samples, the collection process can be made easier by making the amount of required writing samples as small as reasonably possible. Reducing the amount of required writing samples, however, leads to another problem: the probability increases that samples of individual characters will be omitted. Consequently, when characters are omitted from the collected handwriting samples, the ability to achieve accurate handwriting recognition diminishes.